scholarly journals Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xia Qiu ◽  
Xiaoying Zhong ◽  
Honglai Zhang
Keyword(s):  
Supervised Learning ◽  
Link Prediction ◽  
Preferential Attachment ◽  
Strong Support ◽  
Similarity Index ◽  
Tree Model ◽  
Point Selection ◽  
Weighted Network ◽  
Pi Index ◽  
Perception Model

To enhance the depth of excavation and promote the intelligence of acupoint compatibility, a method of constructing weighted network, which combines the attributes of acupoints and supervised learning, is proposed for link prediction. Medical cases of cervical spondylosis with acupuncture treatment are standardized, and a weighted network is constructed according to acupoint attributes. Multiple similarity features are extracted from the network and input into a supervised learning model for prediction. And, the performance of the algorithm is evaluated through evaluation indicators. The experiment finally screened 67 eligible medical cases, and the network model involved 141 acupoint nodes with 1048 edge. Except for the Preferential Attachment similarity index and the Decision Tree model, all other similarity indexes performed well in the model, among which the combination of PI index and Multilayer Perception model had the best prediction effect with an AUC value of 0.9351, confirming the feasibility of weighted networks combined with supervised learning for link prediction, also as a strong support for clinical point selection.

scholarly journals Link Prediction and Node Classification Based on Multitask Graph Autoencoder

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shicong Chen ◽  
Deyu Yuan ◽  
Shuhua Huang ◽  
Yang Chen
Keyword(s):  
Loss Function ◽  
Link Prediction ◽  
Deep Level ◽  
Preferential Attachment ◽  
Similarity Index ◽  
Representation Learning ◽  
High Order ◽  
Fine Tuning ◽  
Common Neighbor ◽  
Node Classification

The goal of network representation learning is to extract deep-level abstraction from data features that can also be viewed as a process of transforming the high-dimensional data to low-dimensional features. Learning the mapping functions between two vector spaces is an essential problem. In this paper, we propose a new similarity index based on traditional machine learning, which integrates the concepts of common neighbor, local path, and preferential attachment. Furthermore, for applying the link prediction methods to the field of node classification, we have innovatively established an architecture named multitask graph autoencoder. Specifically, in the context of structural deep network embedding, the architecture designs a framework of high-order loss function by calculating the node similarity from multiple angles so that the model can make up for the deficiency of the second-order loss function. Through the parameter fine-tuning, the high-order loss function is introduced into the optimized autoencoder. Proved by the effective experiments, the framework is generally applicable to the majority of classical similarity indexes.

scholarly journals Discovering latent node Information by graph attention network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weiwei Gu ◽  
Fei Gao ◽  
Xiaodan Lou ◽  
Jiang Zhang
Keyword(s):  
Supervised Learning ◽  
Link Prediction ◽  
Learning Algorithm ◽  
Network Visualization ◽  
Graph Structure ◽  
High Quality ◽  
Node Centrality ◽  
Influential Nodes ◽  
Node Classification ◽  
Highly Cited

AbstractIn this paper, we propose graph attention based network representation (GANR) which utilizes the graph attention architecture and takes graph structure as the supervised learning information. Compared with node classification based representations, GANR can be used to learn representation for any given graph. GANR is not only capable of learning high quality node representations that achieve a competitive performance on link prediction, network visualization and node classification but it can also extract meaningful attention weights that can be applied in node centrality measuring task. GANR can identify the leading venture capital investors, discover highly cited papers and find the most influential nodes in Susceptible Infected Recovered Model. We conclude that link structures in graphs are not limited on predicting linkage itself, it is capable of revealing latent node information in an unsupervised way once a appropriate learning algorithm, like GANR, is provided.

scholarly journals JKRW Link Prediction – A New Ensemble Technique Based on Merging Other Known Techniques in The Social Network Analysis

2021 ◽  
Vol 15 (12) ◽  
pp. 125
Author(s):  
Aya Taleb ◽  
Rizik M. H. Al-Sayyed ◽  
Hamed S. Al-Bdour
Keyword(s):  
Link Prediction ◽  
Preferential Attachment ◽  
Large Data ◽  
The Other ◽  
Jaccard Coefficient ◽  
Ensemble Technique ◽  
Signed Rank ◽  
Proposed Model ◽  
Different Populations ◽  
A New Technique

In this research, a new technique to improve the accuracy of the link prediction for most of the networks is proposed; it is based on the prediction ensemble approach using the voting merging technique. The new proposed ensemble called Jaccard, Katz, and Random models Wrapper (JKRW), it scales up the prediction accuracy and provides better predictions for different sizes of populations including small, medium, and large data. The proposed model has been tested and evaluated based on the area under curve (AUC) and accuracy (ACC) measures. These measures applied to the other models used in this study that has been built based on the Jaccard Coefficient, Katz, Adamic/Adar, and Preferential attachment. Results from applying the evaluation matrices verify the improvement of JKRW effectiveness and stability in comparison to the other tested models.  The results from applying the Wilcoxon signed-rank method (one of the non-parametric paired tests) indicate that JKRW has significant differences compared to the other models in the different populations at <strong>0.95</strong> confident interval.

Link prediction via layer relevance of multiplex networks

2017 ◽  
Vol 28 (08) ◽  
pp. 1750101 ◽  
Author(s):  
Yabing Yao ◽  
Ruisheng Zhang ◽  
Fan Yang ◽  
Yongna Yuan ◽  
Qingshuang Sun ◽  
...  
Keyword(s):  
Structural Properties ◽  
Link Prediction ◽  
Structural Information ◽  
Similarity Index ◽  
Single Layer ◽  
Structural Features ◽  
Prediction Performance ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Node Similarity

In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can affect that of other layers in multiplex networks. In this paper, the effect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel “Node Similarity Index” based on “Layer Relevance” (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can significantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

scholarly journals Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships

2020 ◽  
Vol 19 ◽  
pp. 117693512094221
Author(s):  
Shahab Bakhtiari ◽  
Sadegh Sulaimany ◽  
Mehrdad Talebi ◽  
Kabmiz Kalhor
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Link Prediction ◽  
Molecular Mechanisms ◽  
Preferential Attachment ◽  
Small Cell ◽  
Bipartite Network ◽  
Small Cell Lung ◽  
Single Nucleotide

Genetic variations such as single nucleotide polymorphisms (SNPs) can cause susceptibility to cancer. Although thousands of genetic variants have been identified to be associated with different cancers, the molecular mechanisms of cancer remain unknown. There is not a particular dataset of relationships between cancer and SNPs, as a bipartite network, for computational analysis and prediction. Link prediction as a computational graph analysis method can help us to gain new insight into the network. In this article, after creating a network between cancer and SNPs using SNPedia and Cancer Research UK databases, we evaluated the computational link prediction methods to foresee new SNP-Cancer relationships. Results show that among the popular scoring methods based on network topology, for relation prediction, the preferential attachment (PA) algorithm is the most robust method according to computational and experimental evidence, and some of its computational predictions are corroborated in recent publications. According to the PA predictions, rs1801394-Non-small cell lung cancer, rs4880-Non-small cell lung cancer, and rs1805794-Colorectal cancer are some of the best probable SNP-Cancer associations that have not yet been mentioned in any published article, and they are the most probable candidates for additional laboratory and validation studies. Also, it is feasible to improve the predicting algorithms to produce new predictions in the future.

scholarly journals Hidden Link Prediction in Criminal Networks Using the Deep Reinforcement Learning Technique

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Marcus Lim ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Mahadevan Supramaniam
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Network Analysis ◽  
Prediction Model ◽  
Supervised Learning ◽  
Link Prediction ◽  
Supervised Machine Learning ◽  
Criminal Networks ◽  
Criminal Network ◽  
Learning Technique

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.

scholarly journals Defining The Best-Fit Machine Learning Classifier Prediction Model For Diagnosis of Heart Disease

2021 ◽  
Author(s):  
Debarati Dey Roy ◽  
Debashis De
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Heart Disease ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Management Service ◽  
Tree Model ◽  
Data Set ◽  
Death Cases ◽  
Best Fit

Abstract Cardio vascular disease or alternatively heart disease is the primitive cause of death all around the world. Last few decades, it was observed that maximum death cases occurred due to heart failure. The heart failure death cases are associated with many risk factors for example high blood pressure, cholesterol level, sugar level etcetera. Therefore, it is advisable that regular and early diagnosis of these factors may reduce the risk of heart failure and hence achieve prompt disease management service. A commonly used technique to process these enormous medical data is called data mining, which help the researchers in health care domain. Several machine learning algorithms are used to analyses these data and help to design the best-fit model for early detection of heart diseases. This research paper contributes various attributes related to heart mal functioning and build the best-fit model using supervised learning algorithm such as various tree (fine tree, medium tree etc), Gaussian Naïve Bayes, Coarse KNN, Medium Gaussian SVM algorithms. In this paper, we used the data set from Kaggle.com. These data set comprises with total 732 instances along with 5 attributes. All these 5 attributes are to be considered for testing purpose and also to find out the best fit model for prediction of heart disease. In this research article we also compare the various classification models based on supervised learning algorithms. Based on the performance and accuracy rate we therefore, choose ‘Medium Tree’ model as the best-fit model. Maximum accuracy is obtained for ‘Medium Tree’ model. The confusion matrix for each model are calculated and analyze.

Sign in / Sign up

Export Citation Format

Share Document